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Survival Support Vector Analysis

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survivalsvm's Introduction

Survival Support Vector Analysis

Cesaire J. K. Fouodo

Introduction

This package performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model.

Installation

Installation from Github:

devtools::install_github("imbs-hl/survivalsvm")

CRAN release coming soon.

Usage

For usage in R, see ?survivalsvm in R. Most importantly, see the Examples section. As a first example you could try

survivalsvm(Surv(time, status) ~ ., veteran, gamma.mu = 0.1)

References

Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K. (2011a). Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinformatics (Oxford, England) 27, 87-94.
Van Belle, V., Pelcmans, K., Van Huffel S. and Suykens J. A.K. (2011b). Support vector methods for survival analysis: a comparaison between ranking and regression approaches. Artificial Intelligence in medecine 53, 107-118.

survivalsvm's People

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mu-bu mnwright sandyyy123 liuwenhaha guhjy lbartels7 xiaojialong0518 1996lixingyu1996

survivalsvm's Issues

Could you help me to see this error?3q

fit = survivalsvm(Surv(OS.time, OS)~., data= est_dd2, gamma.mu = 1)
Error: quadprog needed for this function to work. Please install it.

Error in quadprog::solve.QP(C, -d, t(H), f, meq = meq): constraints are inconsistent, no solution!

Dear Cesaire and Marvin,
Your survivalsvm is an excellent package!When I run survivalsvm() function ,I got this errot:

this is my code:
fit = survivalsvm(Surv(time,OS)~., data= data_train_surv2, gamma.mu = 1,type = "regression")

and this is my data:

I have tried to solve it for a long time without any effect. Could you help me solve the following?
Warmly regards,
Jiaxing Zhang

Sample code fails in setHyperPars2

Please see the issue I opened on your other Github site: imbs-hl/survivalsvm-paper#1. I have not had a response there. Thanks.

Help wanted: control number of parallel processes

Is there a possibility to explicictly control / specify the number of processes / CPU threads that are used when running survivalsvm?

I realized that running survivalsvm::survivalsvm() with the argument opt.meth = "ipop" uses all available CPU resources, whereas running it with opt.meth = "quadprog" seems use one CPU thread only.

Many thanks in advance.

How to extract the prediction weight of each variable

Thanks for the powerful tools, but I was confused about how to measure the importance of each variable. Thus it was difficult to explain which variable plays the main role in the prediction,

As Maverick Meerkat says, the weight can be calculated as t(fit$SV)%*% fit$coefs when the binary classification. So could I use a similar definition t(fit$model.fit$SV) %*% fit$model.fit$Beta as the weight of each variable? Or any other options? Thanks.

How to get feature importance or coefficients in survivalsvm?

Dear Cesaire and Marvin,
Is there a way to get the coefficients or importance (e.g. shapley values) of the features in the survivalsvm model?
Looking forward to your reply.
Warmly regards,
Jay Hua

Error when using kernel = "poly_kernel"

Hi there,
I'm trying to set the poly_kernel in survivalsvm, But I always get this error: Error in cbind(K, -delta * K) : dims [product 36] do not match the length of object [137] In addition: Warning message: In -delta * K : longer object length is not a multiple of shorter object length.
And I can't fing any help about the poly_kernel. Here's the code:

library(survival)
library(survivalsvm)
survsvm.reg <- survivalsvm(formula = Surv(diagtime, status) ~ ., data = veteran,
                           type = "regression", gamma.mu = 0.1,
                           opt.meth = "ipop", kernel = "poly_kernel")

Could you please give me any suggestions? Thank you very much.

survivalsm function fails using another dataset

Hi @mnwright,

Thanks for sharing this package and I've been trying to use it on my own dataset. However, when using the code like this:
survsvm.reg1 <- survivalsvm(Surv(follow_up, event) ~ AGE.bl+PTGENDER+PTEDUCAT , data = study1, type = "regression", gamma.mu = 1, opt.meth = "quadprog", kernel = "add_kernel")
An error shows up:

There are definitely observations in the dataset study1, and follow_up and event are all num.

Could you please let me know where goes wrong? Thanks!

Calculation of the concordance index

I followed the example from your paper (see below) and then tried to compute the concordance index using your function conindex. But I get a much lower value than you achieved. Similarly when I run the same test on my own data I get a very low concordance index using conindex.

How did you calculate the c-index values reported in your paper?
Am I doing something wrong?

data(veteran, package = "survival")
set.seed(123)
n <- nrow(veteran)
train.index <- sample(1:n, 0.7 * n, replace = FALSE)
test.index <- setdiff(1:n, train.index)

survsvm.reg <- survivalsvm(Surv(diagtime, status) ~ .,
                           subset = train.index, data = veteran,
                           type = "regression", gamma.mu = 1,
                           opt.meth = "quadprog", kernel = "add_kernel")

print(survsvm.reg)

pred.survsvm.reg <- predict(object = survsvm.reg,
                            newdata = veteran, subset = test.index)

print(pred.survsvm.reg)
conindex(pred.survsvm.reg, veteran$time[test.index])

survivalsvm result

Call:

 survivalsvm(Surv(diagtime, status) ~ ., subset = train.index, data = veteran, type = "regression", gamma.mu = 1, opt.meth = "quadprog", kernel = "add_kernel") 

Survival svm approach              : regression 
Type of Kernel                     : add_kernel 
Optimization solver used           : quadprog 
Number of support vectors retained : 39 
survivalsvm version                : 0.0.5 


survivalsvm prediction

Type of survivalsvm                      : regression 
Type of kernel                           : add_kernel 
Optimization solver used in model        : quadprog 
predicted risk ranks                     : 13.89 14.95 11.12 15.6 10.7 ...
survivalsvm version                      : 0.0.5 
  C Index 
0.5005848

prediction should warn if/which rows are removed

see:

survivalsvm/R/predictions.R

Line 133 in d0eec0d

X_pred <- X_pred[stats::complete.cases(X_pred),]

library(survival)
library(survivalsvm)

set.seed(123)
n <- nrow(veteran)

train.index <- sample(seq_len(n), 0.7*n, replace = FALSE)
test.index <- setdiff(seq_len(n), train.index)

train <- veteran[train.index, , drop = FALSE]
test <- veteran[test.index, , drop = FALSE]

test[1, "karno"] <- NA_real_

survsvm.reg <- survivalsvm(
  Surv(diagtime, status) ~ .,
  data = train,
  type = "regression",
  gamma.mu = 1,
  opt.meth = "quadprog",
  kernel = "add_kernel"
)

pred.survsvm.reg <- predict(
  object = survsvm.reg,
  newdata = test
)

conindex(
  pred.survsvm.reg,
  Surv(test$diagtime, test$status)
)
#> Error in conindex(pred.survsvm.reg, Surv(test$diagtime, test$status)): Error: lengths do not macht.


complete_rows <- complete.cases(test)
conindex(
  pred.survsvm.reg,
  Surv(
    test$diagtime[complete_rows],
    test$status[complete_rows]
  )
)
#>   C Index 
#> 0.7109905

^{Created on 2022-04-27 by the reprex package (v2.0.1)}

Session info

sessioninfo::session_info()
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#>  date     2022-04-27
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#> 
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Feature selection with Survival-SVM

Dear maintainers,

I'm using this excellent package to conduct my research. This package has implemented the SVM methods for survival analysis from Van Belle et al. According to their Bioinformatics paper (2011): "Improved performance on high-dimensional survival data by application of Survival-SVM", a feature selection technique is proposed in Section 2.3.2 & 2.3.3.

Is this package able to deal with this part? Or do you intend to implement this useful technique in the future?

Best regards,
Darren Tsai

Cross Validation

Dear author, thank you for creating this valuable tool.
I would like to know how to run this method using 5-fold or 10-fold cross-validation, as it can help me determine the optimal parameters.
I hope to receive your help.
Best wishes.

Clinical Kernel

I have been using 'survivalsvm' but have just learnt from this paper https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2010-16.pdf about the idea of a clinical kernel for survivalsvm. I was wondering if the clinical kernel has been implemented in R and if so how would I access it. Thanks.

imbs-hl / survivalsvm Goto Github PK

survivalsvm's Introduction

Survival Support Vector Analysis

Introduction

Installation

Usage

References

survivalsvm's People

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